The municipal solid waste generation distribution prediction system based on FIG–GA-SVR model

Dai, Feng; Nie, Guihua; Chen, Yi

doi:10.1007/s10163-020-01022-5

Cited by 22 publications

(15 citation statements)

References 51 publications

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“…In addition, in this study, the DWGR prediction models were developed using single algorithms. In recent years, however, studies have focused on improving the performance of single algorithms by developing various hybrid ML models [11,[14][15][16]55]. Therefore, based on the results of this study, there is also room for improvement in the proposed model, and further research will be required to improve the predictive performance.…”

Section: Discussion and Recommendationsmentioning

confidence: 91%

“…where n indicates the number of the observed data. N increases with observations falling between the corresponding X L and X U increase; the "Bracketed by 95 PPU" denotes the number of observed data bracketed by a 95% confidence interval, and this value is equal to 100 when all of the observed data are within the range of X L ≤ N ≤ X U [50][51][52][53][54][55].…”

Section: Model Uncertainty Analysismentioning

confidence: 99%

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Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas

Cha

Choi

Hong

et al. 2022

IJERPH

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Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition waste generation rate (DWGR) of buildings in redevelopment areas in South Korea. Various ML algorithms (i.e., artificial neural network (ANN), K-nearest neighbors (KNN), linear regression (LR), random forest (RF), and support vector machine (SVM)) were applied to the development of an optimal predictive model, and the main hyper parameters (HPs) for each algorithm were optimized. The results suggest that ANN-ReLu (coefficient of determination (R2) 0.900, the ratio of percent deviation (RPD) 3.16), SVM-polynomial (R2 0.889, RPD 3.00), and ANN-logistic (R2 0.883, RPD 2.92) are the best ML models for predicting the DWGR. They showed average errors of 7.3%, 7.4%, and 7.5%, respectively, compared to the average observed values, confirming the accurate predictive performance, and in the uncertainty analysis, the d-factor of the models appeared less than 1, showing that the presented models are reliable. Through a comparison with ML algorithms and HPs applied in previous related studies, the results herein also showed that the selection of various ML algorithms and HPs is important in developing optimal ML models for WG management.

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Section: Discussion and Recommendationsmentioning

confidence: 91%

Section: Model Uncertainty Analysismentioning

confidence: 99%

Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas

Cha

Choi

Hong

et al. 2022

IJERPH

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“…Moreover, DM algorithms present methods to identify changes and trends in the generation of household waste based on operational data, making it possible to determine the influence of data, such as the total population (Ceylan, 2020;Oliveira et al, 2019), annual income per capita (Ceylan, 2020;Dai et al, 2020), literacy rate (Kolekar et al, 2017;Pérez-López et al, 2016), age group (Kannangara et al, 2018;Kolekar et al, 2017) and monthly consumption (Dai et al, 2020) expenses in the temporal variability of MSW generation (Kolekar et al, 2017). They also make it possible to establish a relationship between the rate of plastic waste generation and socioeconomic groups (Wu et al, 2020).…”

Section: Data Mining To Support Decision-making For the Collection And Transport Of Solid Wastementioning

confidence: 99%

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

et al. 2021

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The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.

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“…Although certain methods can be used to develop superior prediction models with improved prediction performance, these approaches pose considerable limitations. Recent research have focused on the development of hybrid AI models [13,14,[18][19][20][21]24,47] to overcome the limitations associated with the existing standalone algorithms and augment the predictive performance of AI models. To this end, Abbasi et al (2013Abbasi et al ( , 2014 [20,21], Cai et al (2020) [24], Dai et al (2020) [47], Golbaz et al (2019) [13], and Song et al (2017) [18] developed hybrid models with improved predictive performance to predict C&DW and MSW generation by applying the following algorithms: the SVM algorithm, wavelet denoising method (WT), partial least-squares (PLS), long-and short-term memory (LSTM), fuzzy information granulation-genetic algorithm (FIG-GA), fuzzy, and gray model (GM).…”

Section: Introductionmentioning

confidence: 99%

“…Recent research have focused on the development of hybrid AI models [13,14,[18][19][20][21]24,47] to overcome the limitations associated with the existing standalone algorithms and augment the predictive performance of AI models. To this end, Abbasi et al (2013Abbasi et al ( , 2014 [20,21], Cai et al (2020) [24], Dai et al (2020) [47], Golbaz et al (2019) [13], and Song et al (2017) [18] developed hybrid models with improved predictive performance to predict C&DW and MSW generation by applying the following algorithms: the SVM algorithm, wavelet denoising method (WT), partial least-squares (PLS), long-and short-term memory (LSTM), fuzzy information granulation-genetic algorithm (FIG-GA), fuzzy, and gray model (GM). Furthermore, Liang et al (2021) [14] and Soni et al (2019) [19] improved the ANN model performance using Archimedes' optimization algorithm (AOA)-ANN and GA-ANN hybrid models, which enhanced the performance of the MSW prediction model as well.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate

2023

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Owing to the rapid increase in construction and demolition (C&D) waste, the information of waste generation (WG) has been advantageously utilized as a strategy for C&D waste management. Recently, artificial intelligence (AI) has been strategically employed to obtain accurate WG information. Thus, this study aimed to manage demolition waste (DW) by combining three algorithms: artificial neural network (multilayer perceptron) (ANN-MLP), support vector regression (SVR), and random forest (RF) with an autoencoder (AE) to develop and test hybrid machine learning (ML) models. As a result of this study, AE technology significantly improved the performance of the ANN model. Especially, the performance of AE (25 features)–ANN model was superior to that of other non-hybrid and hybrid models. Compared to the non-hybrid ANN model, the performance of AE (25 features)–ANN model improved by 49%, 27%, 49%, and 22% in terms of the MAE, RMSE, R2, and R, respectively. The hybrid model using ANN and AE proposed in this study showed useful results to improve the performance of the DWGR ML model. Therefore, this method is considered a novel and advantageous approach for developing a DWGR ML model. Furthermore, it can be used to develop AI models for improving performance in various fields.

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The municipal solid waste generation distribution prediction system based on FIG–GA-SVR model

Cited by 22 publications

References 51 publications

Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas

Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate

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